NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh

• 作者: Abdullah Al Jami Ayesha Ferdous Mita Khairul Hasan Meher Uddin Himel Shilpy Rani Basak
• 来源期刊: 地下水科学与工程:英文版
• 学科: 地球科学
• 关键词: NARX neural networks Artificial neural networks Groundwater level Levenberg-Marquardt Algorithm(LMA) Bayesian Regularization Algorithm(BRA)
• 年，卷（期）: 2020,（2）
• 页码范围: 118-126
• 页数: 9页
• 分类号: P641.7

摘要

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R~2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.